Optimal averages for nonlinear signal decompositions - Another alternative for empirical mode decomposition. - Feng Zhou 018; Lijun Yang; Haomin Zhou 001; Lihua Yang (Signal Process., 2016)

Hilbert spectrum analysis of piecewise stationary signals and its application to texture classification. - Zhihua Yang; Qian Zhang 044; Feng Zhou 018; Lihua Yang (Digit. Signal Process., 2018)

Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices. - Feng Zhou 018; Qun Zhang 002; Didier Sornette; Liu Jiang (Appl. Soft Comput., 2019)

EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction. - Feng Zhou 018; Haomin Zhou 001; Zhihua Yang; Lihua Yang (Expert Syst. Appl., 2019)

A 2-Stage Strategy for Non-Stationary Signal Prediction and Recovery Using Iterative Filtering and Neural Network. - Feng Zhou 018; Haomin Zhou 001; Zhihua Yang; Lihua Yang (J. Comput. Sci. Technol., 2019)

A New Credit Scoring Model Based on Prediction of Signal on Graph. - Zhihua Yang; Qian Zhang 044; Feng Zhou 018; Lihua Yang (ICPRAI, 2020)

Segmentation of Pulmonary Nodules Using a GMM Fuzzy C-Means Algorithm. - Xiangxia Li; Bin Li 024; Fang Liu 030; Hua Yin; Feng Zhou 018 (IEEE Access, 2020)

A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis. - Zhihua Yang; Qian Zhang 044; Feng Zhou 018 (IEEE Access, 2020)

A new prediction method for recommendation system based on sampling reconstruction of signal on graph. - Zhihua Yang; Feng Zhou 018; Lihua Yang; Qian Zhang 044 (Expert Syst. Appl., 2020)

IF2CNN: Towards non-stationary time series feature extraction by integrating iterative filtering and convolutional neural networks. - Feng Zhou 018; Haomin Zhou 001; Zhihua Yang; Linyan Gu (Expert Syst. Appl., 2021)

Attention enhanced long short-term memory network with multi-source heterogeneous information fusion: An application to BGI Genomics. - Qun Zhang 002; Lijun Yang; Feng Zhou 018 (Inf. Sci., 2021)

Intraday and interday features in the high-frequency data: Pre- and post-Crisis evidence in China's stock market. - Qun Zhang 002; Peihui Zhang; Feng Zhou 018 (Expert Syst. Appl., 2022)

Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks. - Linyan Gu; Lihua Yang; Feng Zhou 018 (Neural Networks, 2022)

RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network. - Feng Zhou 018; Antonio Cicone; Haomin Zhou 001 (CoRR, 2023)

RRCNN+: An Enhanced Residual Recursive Convolutional Neural Network for Non-stationary Signal Decomposition. - Feng Zhou 018; Antonio Cicone; Haomin Zhou 001 (CoRR, 2023)

T2V_TF: An adaptive timing encoding mechanism based Transformer with multi-source heterogeneous information fusion for portfolio management: A case of the Chinese A50 stocks. - Feng Zhou 018; Qun Zhang; Yuan Zhu; Tian Li (Expert Syst. Appl., 2023)

Refinements of Approximation Results of Conditional Restricted Boltzmann Machines. - Linyan Gu; Lihua Yang; Feng Zhou 018 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Automatic feature learning model combining functional connectivity network and graph regularization for depression detection. - Lijun Yang; Xiaoge Wei; Fengrui Liu; Xiangru Zhu; Feng Zhou 018 (Biomed. Signal Process. Control., 2023)

IRCNN: A novel signal decomposition approach based on iterative residue convolutional neural network. - Feng Zhou 018; Antonio Cicone; Haomin Zhou 001 (Pattern Recognit., 2024)